As a data scientist, you have to solve complex business-related problems using your technical skills. Collecting large amounts of data and transforming it into a more useful format, working with different programming languages, having a clear understanding of statistics and analytical techniques are just parts of your job description. Here is where having a solid toolbox would make your job as a data scientist much easier and efficient.
Read on to find out which is the most essential software you should know about as a data scientist.
Relational databases structure the data in tables and to work with them you would most likely use SQL – Structured Query Language.
There are a lot of applications that manage the data and structure in relational databases and most of them use techniques such as machine learning and data analytics.
Microsoft’s SQL Server has been constantly evolving for the past 20 years. It offers various services, including embedded R code and Python support. It can be used even by data scientists that have no previous experience with Transact SQL.
MySQL is a popular free open-source software currently owned by Oracle. It is easy to install and comes with a huge development community, plenty of documentation, and tools that make management easy. It can be integrated with almost every reporting or visualization tool you use.
PostgreSQL is another open-source option that offers flexibility and supports complex queries and multi-environments. It works very well with big data and supports unstructured data.
NoSQL data stores use low-level languages to allow faster access to non-tabular data structures such as documents, graphs, wide columns, and key values. There are no standard specifications for NoSQL and some of the databases offer support for SQL.
A popular NoSQL database system that offers high flexibility and scalability. It stores the data in a non-structured way as JSON documents and allows the data structure to be changed over time.
Another open-source NoSQL database that also functions as cache memory and supports numerous data structures. It offers high performance and is very suitable for data-intensive tasks.
Big Data frameworks
These frameworks come in handy when you have to analyze a big amount of data effectively.
Offering high availability and fast access, this framework is dealing with all the complexity of processing and storing big data. Its algorithm divides the tasks into smaller bits and then allocates them between available computer clusters in a distributed environment.
Another leader when it comes to big data, Spark offers great analytics speed and is very easy to use. It requires fewer machines to process the same amount of data, compared to Hadoop, and offers real-time processing.
Data visualization tools use the pre-processed data and transform it into more understandable forms. The most popular is still Microsoft Excel, but there are other tools that offer more functionality.
This visualization tool by Microsoft allows you to use data from different sources, including online services, and generate interactive dashboards with tables, charts, and other visualization objects.
Another tool for creating interactive dashboards using multiple data sources. Suitable even for non-technical users with plenty of tutorials, it offers designs that are optimized for mobile and unlimited data connectors.
This tool comes with a very clean interface and saves you time by helping you focus on the most important data and find new insights by using comprehensible visual elements.
These three most popular web scraping tools allow you to extract data from webpages and use it for further analysis.
This web scraper is a desktop application with a very user-friendly UI. Using a graphical designer it allows you to visualize the data extraction process and in addition to the desktop version, it offers a cloud-based service that makes the process 4 to 10 times faster.
Content Grabber requires coding skills but offers advanced functionality, including debugging interfaces and scripting editing. You can also wite regular expressions using .Net languages and add scarping capabilities to desktop and web applications through the API.
These programming languages are created clearly with the focus on data science and help to deal with massive data analysis.
The most preferred language for data science, Phyton offers great performance and scripting potential and is simple and easy to learn. It is the perfect starting point if you want to advance fast in application development.
Used mostly for graphing and processing of statistical data, R language is becoming more popular recently because it offers a lot of potential when it comes to data mining and analytics. It comes with a great library of free packages to further expand its functionalities.
An IDE (Integrated Development Environment) puts together everything you use in your working process. Just make sure the one you choose is compatible with your preferred language.
Spyder offers more than just IDE functionality with additional tools for data exploration and visualization. It is perfect for data scientists who need to code as it supports numerous languages and comes with a debugger to help you interactively trace every code line.
PyCharm is the go-to IDE if you use Python for programming. It can be integrated with all main version control systems and comes with great functionalities such as code completion, smart search, and error fixing. It supports a lot of scientific packages and can be integrated into Docker and Vagrant.
If you prefer R language, RStudio is the IDE for you as it offers plenty of features including code completion and syntax highlighting. It can be installed on a desktop or run from a web browser and comes with a debugging mode in which you can see dynamically how the data is being updated when you execute a program step by step.
In conclusion, these 18 tools are everything you need if you want to up your performance in data science. Don’t forget to keep an eye for new technologies and trends as the field is very fast developing and changes are quite often.